Our proposed model has advantages in precisely dealing with the reset dynamics. The resetting behavior at the moment of firing is one of the factors that significantly influence the information processing mechanism. In general, studies addressing supervised learning of multi-spike SNNs have introduced double-reset neuron models, which reset the membrane potential and also the corresponding postsynaptic current at the same time due to its simplicity [1]}, [2]}. However, there are concerns that the adoption of double-reset means that any information input up to each spike timing is lost on each firing, completely breaking the dependency between previous and subsequent spikes within the same neuron, thus lacking the benefits of multi-spike. By contrast, we reset only the membrane potential that has reached the threshold to the baseline, 0, while the postsynaptic current is not reset and retains its pre-firing values. In this way, the past information received by the neuron itself is propagated into the future via both its own past spike timing and the postsynaptic current path. There is a non-negligible difference in the network structure whether double-reset is employed or not, and we employed the latter in this study.
